IEEE Working Group on Energy Forecasting and Analytics
-Forecasting objectives: load, renewable energy, price; individual consumer load, demand response, EV charging load, net load, wind power ramp; power, gas, heat, and cooling demands; reserve capacity, risk, network congestion;
-Forecasting algorithms: traditional regression, advanced machine learning, deep learning, transfer learning, ensemble learning, robust forecasting;
-Forecasting outputs: point forecasting, probabilistic forecasting, hierarchical forecasting, cost-oriented forecasting;
-Forecasting evaluation: Alternative loss functions for different forecasting objectives and different applications.
-Data preprocessing: outlier detection, data cleansing, feature selection, data compression;
-Behavior modeling: load profiling, energy theft detection, renewable energy spatiotemporal correlation analysis, pattern recognition, sensitivity analysis, load or renewable energy simulation;
-Applications: demand response implementation, data-driven pricing, bidding, and trading, topology identification, outage and risk management, privacy concerns.
-Chair: Prof. Hamid Zareipour, University of Calgary, Canada (Secretary: 2012 – 2016; Vice Chair: 2016-2019)
-Secretary: Dr. Yi Wang, ETH Zurich, Switherland
-Past-chair: Prof. Tao Hong, University of North Carolina at Charlotte, US (Chair: 2011-2019)
-Past Officers: Dr. Shu Fan, Monash University (Vice Chair: 2011-2016)